Abstract ：

Obtaining the plasma transport coefficients of the mixed system of arc-extinguishing medium and erosion product is the precondition of numerical simulation of erosion-diffusion in an arc chamber. In this article, the minimum Gibbs free energy method was used to calculate the composition of the H2-Cu/Al2O3 system, including solid and liquid copper (aluminum oxide), because copper and aluminum oxide are commonly used as electrode and ceramic materials in hydrogen dc contactors, respectively. The transport coefficients of H2-Cu/Al2O3 system and the phase transition of erosion products provide the basis for the subsequent simulation of erosion-diffusion of electrode and arc chamber in the H2 plasma. Results revealed that: 1) in the low-temperature range, copper (aluminum oxide) mainly exists in the form of a solid or a liquid state, resulting in the transport coefficients of the H2-Cu/Al2O3 system consistent with that of the H2 system; 2) as temperature increases, the copper (aluminum oxide) undergoes gasification (sublimation-decomposition) reaction that exists in a gaseous state and begins to affect the transport properties of the H2-Cu/Al2O3 system; in particular, the proportion of copper (aluminum oxide) has the most significant effect on viscosity; and 3) as the temperature continues to rise, atoms and molecules in the H2-Cu/Al2O3 system are completely ionized and the proportion of copper (aluminum oxide) has little effect on the transport properties (except electrical conductivity) of the H2-Cu/Al2O3 system. Finally, the transport coefficients of H2-Cu/Al2O3 plasma mixtures are reliable enough, in engineering application aspect, to be applied for simulation of erosion-diffusion in hydrogen dc contactors. © 1973-2012 IEEE.

Keyword ：

DC contactor; hydrogen; plasma; transport coefficient

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Abstract ：

Several deep learning partial discharge (PD) diagnostic approaches have been developed in recent years to guarantee the security and stability of gas-insulated switchgear (GIS). The centralized training method requires multiple clients to jointly obtain as much data as possible to train the model to achieve excellent performance, which is impractical due to conflicts of interest and privacy protection. Furthermore, because of differences in the distribution of client data and the presence of a small sample, achieving high-precision and robust diagnosis for each client is an urgent problem. To that end, a novel personalized federated meta-learning (FML) is proposed in this paper to address the aforementioned issues. It develops reliable and personalized PD diagnosis models by collaborating with multiple clients and solves the problem of small sample diagnosis through scenario training under the premise of protecting data privacy. The experimental results show that the FML proposed can diagnose GIS PD with high precision and robustness for each client while maintaining privacy. The diagnostic accuracy of the FML proposed in this paper, especially for on-site unbalanced small sample clients, is 93.07%, which is significantly higher than that for other methods. It serves as a model for the collaborative development of an effective GIS PD diagnostic model. © 2023 Author(s).

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Abstract ：

The computational model of textured surfaces is highly nonlinear, and the texturing parameters interact with each other. The Fourier Amplitude Sensitivity Test(FAST) method was introduced to calculate the first-order sensitivity indices of texturing parameters to the tribological characteristics. Results show that both the inlet suction and the cumulative effect influence the tribological performance, but which mechanism plays the leading role is related to the convergence ratio. Besides, the texture density along the velocity direction is the most crucial geometric parameter; the effects of the cell size and the bottom profile are negligible. The sensitivity indices provide a novel perspective to quantify the influence of input on the output and the FAST method proved ideally suited to the texturing model. © 2022

Keyword ：

FAST; Global sensitivity analysis; Textured surfaces; Tribological performance

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Abstract ：

5xxx aluminum alloys find wide application in different industry fields due to their several advantages such as good weldability and corrosion resistance. However, their strength is inferior to their 2xxx and 7xxx counterparts, limiting their application to high load-bearing conditions. In order to improve the mechanical properties of additively manufactured Al5183 aluminum alloy, titanium fiber-reinforced Aluminum (TFRA) components were fabricated for the first time by wire-based directed energy deposition-arc manufacturing (DED-arc) using a dual-wire feeding system. The reinforcing titanium fiber was kept in solid state by carefully controlling its feed path and arc heat input. The thickness of the interface between the titanium alloy wire and the aluminum alloy matrix was about 3–10 µm, with a gradient transition in chemical composition and no obvious cracking tendencies. The results reveal that, as compared to the non-fiber-reinforced aluminum components, the yield and tensile strength of the TFRA components increased by 124 % and 33 % respectively, by adding 10.5 % volume fraction of titanium fibers. Meanwhile, its impact energy increased by 128 %, from the original value of 7.9–18.0 J. The increased strength was analytically analyzed by the mixed law theory and has been verified by finite element simulation. The increased impact property of the TFRA components is due to the fact that crack propagation in the aluminum matrix is blocked by the titanium fiber. Therefore, this work provides a promising way to fabricate high strength aluminum alloy with continuous fiber through DED-arc. © 2023 Elsevier B.V.

Keyword ：

Aluminum and titanium alloy; Directed energy deposition-arc manufacturing (DED-arc); Microstructure evolution; Reinforcement mechanism; Titanium fiber- reinforced Aluminum

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Abstract ：

The data-driven models have achieved remarkable advancements in diagnosing gas-insulated substation (GIS) insulation defects on specific massive datasets. However, restricted by field operating conditions and sample scarcity of GIS, the above methods are difficult to achieve high-precision and robust diagnosis of GIS insulation defects on-site. To settle these problems, we propose an innovative domain adversarial graph convolutional network (DAGCN) for few-shot GIS insulation defects on-site. To improve the accuracy of GIS insulation defects diagnosis, a graph convolutional network (GCN) with multiple attention mechanisms is constructed to take advantage of node features and topology to adaptively mine defect features. Then, a novel domain adversarial (DA) tactic is introduced for GIS insulation defect diagnosis in few-shot conditions through the transfer of diagnostic knowledge. In the proposed DA, the source and target domains classifier loss are jointly optimized to realize feature migration and domain matching. Experiment results demonstrate that the DAGCN achieves reliable diagnosis of GIS insulation defect, which has obvious advantages over existing methods. Simultaneously, the DAGCN proposed can achieve satisfactory diagnosis including unlabeled field samples. Moreover, it has development potential in diagnosing unlabeled samples, thus providing a feasible solution for GIS insulation defect diagnosis and GIS operation and maintenance. © 1986-2012 IEEE.

Keyword ：

domain adversarial strategy; gas-insulated substation; Graph convolutional network; insulation defect diagnosis; multiple attention mechanisms

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Abstract ：

To investigate the carbapenemases distribution of carbapenem-resistant Klebsiella pneumoniae (CRKP) in the intensive care unit, and the clinical characteristics between carbapenem-resistant hypervirulent Klebsiella pneumoniae (CR-hvKP) and carbapenem-resistant non-hypervirulent Klebsiella pneumoniae...

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Background and Aim: The COVID-19 pandemic imperiled the global health system. We aimed to determine the impact of COVID-19 on the care continuum of HCV-infected patients.(#br)Methods: 256 Patients who were prescribed a course of DAA therapy at three tertiary medical centers in the US and China betwe...

Keyword ：

COVID-19 pandemic direct-acting antivirals HCV care continuum telemedicine viral hepatitis elimination

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Abstract ：

To solve the multiscale problem of modelling surface texture with mass-conserving cavitation, a new method combining the finite cell method with the Fischer–Burmeister–Newton–Schur algorithm (FCM-FBNS) was developed. Numerical experiments confirmed that the proposed method has strong robustness and ...

Keyword ：

Finite element method Mass-conserving cavitation Multiscale method Surface texture

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Abstract ：

Data-driven diagnosis methods have been systematically investigated for the diagnosis of gas-insulated switchgear (GIS) partial discharge (PD). However, because of the scarcity of samples on-site, an operational gap exists between the diagnostic methods and their actual application. To settle this issue, a novel metric-based meta-learning (MBML) method is proposed. First, a hybrid self-attention convolutional neural network is constructed for feature extraction and trained through supervised learning. Then, the episodic MBML is used to train other parts, and the metric classifier is employed for diagnosis. The proposed MBML exhibits an accuracy of 93.17% under 4-way 5-shot conditions, which is a significant improvement over traditional methods. When the number of support sets is small, the benefits of MBML are more prominent, providing a viable solution for the on-site diagnosis of PD in GISs. © 2022 ISA

Keyword ：

Convolutional neural network; Few-shot; Gas-insulated switchgear; Metric-based meta-learning; Partial discharge diagnosis

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The purpose of this paper is to determine the distribution of the breakdown locations between plane-plane electrodes in the conditioning progress of the vacuum interrupter. The material of the experimental plane-plane electrodes was CuCr50. The gap length between electrodes was set at 1.0 mm. A standard negative lightning impulse voltage was applied. The breakdown location was decided by the breakdown photo at different perspectives by a high-speed camera, with recording speed of 5.8 × 105 frames/s. The experimental results indicate that the conditioning area covered the entire surface of the contact during the conditioning process. The distribution of breakdown locations was random distribution during the conditioning progress. In the initial conditioning progress of a small vacuum gap, the breakdown locations weren’t only distributed in the region where the electric field was highest. The strength of the electric field might not be the primary key factor that determined the breakdown position distribution during the conditioning process. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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